<p>Sentiment analysis and opinion ranking methods have emerged as powerful tools to extract valuable information from user-generated data such as reviews, comments, and articles. However, optimizing sentiment analysis and opinion ranking remains a challenge due to the vast amount of available data and the need to accurately assess the sentiment orientation of opinions. To address this challenge, we propose the Bio-SentioRank algorithm, a feature-driven approach that combines bio-inspired optimization techniques with sentiment analysis and ranking methodologies. The algorithm aims to efficiently extract, analyze, and rank opinions based on their relevance and sentiment orientation. The Bio-SentioRank algorithm leverages a Layer Recurrent Neural Network (LRNN) for feature extraction, enabling it to capture the sequential information and context within opinions. In the ranking step, Bio-SentioRank incorporates the Crossover Boosted Improved Manta Ray Foraging Optimization (CBIMRFO) algorithm. CBIMRFO enhances the ranking process by leveraging crossover operations and mimicking the foraging behavior of manta rays to identify the most relevant and influential opinions. Our experimental evaluation was conducted on the datasets namely the Movie reviews dataset and the Yelp reviews dataset. Comparative analysis with existing techniques showcases the superiority of Bio-SentioRank in terms of recall, accuracy, precision, and ranking metrics. The Bio-SentioRank algorithm achieved accuracy, precision, recall, and F1-score of 97.5%, 96.5%, 98%, and 97% respectively in the Movie reviews dataset. Also, the Bio-SentioRank algorithm obtained an accuracy of 98%, precision of 96%, recall of 97.5%, and F1-score of 97.5% in the Yelp dataset.</p>